OpenRaiser/Intern-Atlas huggingface dataset:
Buying time in software development: how estimates become commitments?
Venue: arXiv cs.SE 2021
Year: 2021

Despite years of research for improving accuracy, software practitioners still face software estimation difficulties. Expert judgment has been the prevalent method used in industry, and researchers' focus on raising realism in estimates when using it seems not to be enough for the much-expected improvements. Instead of focusing on the estimation process's technicalities, we investigated the interaction of the establishment of commitments with customers and software estimation. By observing estimation sessions and interviewing software professionals from companies in varying contexts, we found that defensible estimates and padding of software estimates are crucial in converting estimates into commitments. Our findings show that software professionals use padding for three different reasons: contingency buffer, completing other tasks, or improving the overall quality of the product. The reasons to pad have a common theme: buying time to balance short- and long-term software development commitments, including the repayment of technical debt. Such a theme emerged from the human aspects of the interaction of estimation and the establishment of commitments: pressures and customers' conflicting short and long-term needs play silent and unrevealed roles in-between the technical activities. Therefore, our study contributes to untangling the underlying phenomena, showing how the practices used by software practitioners help to deal with the human and social context in which estimation is embedded.
A Scalable Concurrent Algorithm for Dynamic Connectivity
Venue: arXiv cs.DS 2021
Year: 2021

Dynamic Connectivity is a fundamental algorithmic graph problem, motivated by a wide range of applications to social and communication networks and used as a building block in various other algorithms, such as the bi-connectivity and the dynamic minimal spanning tree problems. In brief, we wish to maintain the connected components of the graph under dynamic edge insertions and deletions. In the sequential case, the problem has been well-studied from both theoretical and practical perspectives. However, much less is known about efficient concurrent solutions to this problem. This is the gap we address in this paper. We start from one of the classic data structures used to solve this problem, the Euler Tour Tree. Our first contribution is a non-blocking single-writer implementation of it. We leverage this data structure to obtain the first truly concurrent generalization of dynamic connectivity, which preserves the time complexity of its sequential counterpart, but is also scalable in practice. To achieve this, we rely on three main techniques. The first is to ensure that connectivity queries, which usually dominate real-world workloads, are non-blocking. The second non-trivial technique expands the above idea by making all queries that do not change the connectivity structure non-blocking. The third ingredient is applying fine-grained locking for updating the connected components, which allows operations on disjoint components to occur in parallel. We evaluate the resulting algorithm on various workloads, executing on both real and synthetic graphs. The results show the efficiency of each of the proposed optimizations; the most efficient variant improves the performance of a coarse-grained based implementation on realistic scenarios up to 6x on average and up to 30x when connectivity queries dominate.
A Neat Linked Queue with the Rear Blank Node
Venue: arXiv cs.DS 2021
Year: 2021

We introduce a very simple queue implementation with the singly linked list. With the help of the rear blank node instead of the usual header node, we avoid additional check steps for the dequeue operation in the traditional implementations existing for many decades. The essence of our representation is the half-opened pointer interval with the same direction of the queue operations, which can guarantee the uniform treatment even in the empty queue case. The simplification of queue implementations cuts off unnecessary steps, and it minimizes the number of steps in the dequeue operation with the time limitation of enqueue operation, which could contribute to the performance of the real-time systems. We extend the linked queue to the circularly linked queue, which can also be used to implement stack and take advantage of the maximal information of the single direction in the circularly linked list, and it actually constructs the output-restricted deque. We also present a variant: lazy circularly linked queue, which is more efficient in some special cases, especially for the dequeue operations.
Learning Disentangled Representations for Time Series
Venue: arXiv cs.LG 2021
Year: 2021

Time-series representation learning is a fundamental task for time-series analysis. While significant progress has been made to achieve accurate representations for downstream applications, the learned representations often lack interpretability and do not expose semantic meanings. Different from previous efforts on the entangled feature space, we aim to extract the semantic-rich temporal correlations in the latent interpretable factorized representation of the data. Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals. To bridge the gap, we propose Disentangle Time Series (DTS), a novel disentanglement enhancement framework for sequential data. Specifically, to generate hierarchical semantic concepts as the interpretable and disentangled representation of time-series, DTS introduces multi-level disentanglement strategies by covering both individual latent factors and group semantic segments. We further theoretically show how to alleviate the KL vanishing problem: DTS introduces a mutual information maximization term, while preserving a heavier penalty on the total correlation and the dimension-wise KL to keep the disentanglement property. Experimental results on various real-world benchmark datasets demonstrate that the representations learned by DTS achieve superior performance in downstream applications, with high interpretability of semantic concepts.
Teaching Inclusive Engineering Design at a Small Liberal Arts College
Venue: arXiv cs.CY 2021
Year: 2021

Modern engineering education tends to focus on mathematics and fundamentals, eschewing critical reflections on technology and the field of engineering. In this paper, I present an elective engineering course and a 3-lecture module in an introductory course that emphasize engaging with the social impacts of technology.
Synthesising Multi-Modal Minority Samples for Tabular Data
Venue: arXiv cs.LG 2021
Year: 2021

Real-world binary classification tasks are in many cases imbalanced, where the minority class is much smaller than the majority class. This skewness is challenging for machine learning algorithms as they tend to focus on the majority and greatly misclassify the minority. Adding synthetic minority samples to the dataset before training the model is a popular technique to address this difficulty and is commonly achieved by interpolating minority samples. Tabular datasets are often multi-modal and contain discrete (categorical) features in addition to continuous ones which makes interpolation of samples non-trivial. To address this, we propose a latent space interpolation framework which (1) maps the multi-modal samples to a dense continuous latent space using an autoencoder; (2) applies oversampling by interpolation in the latent space; and (3) maps the synthetic samples back to the original feature space. We defined metrics to directly evaluate the quality of the minority data generated and showed that our framework generates better synthetic data than the existing methods. Furthermore, the superior synthetic data yields better prediction quality in downstream binary classification tasks, as was demonstrated in extensive experiments with 27 publicly available real-world datasets
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
Venue: arXiv cs.CL 2021
Year: 2021

The capacity of empathy is crucial to the success of open-domain dialog systems. Due to its nature of multi-dimensionality, there are various factors that relate to empathy expression, such as communication mechanism, dialog act and emotion. However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling. In this paper, we propose a multi-factor hierarchical framework, CoMAE, for empathetic response generation, which models the above three key factors of empathy expression in a hierarchical way. We show experimentally that our CoMAE-based model can generate more empathetic responses than previous methods. We also highlight the importance of hierarchical modeling of different factors through both the empirical analysis on a real-life corpus and the extensive experiments. Our codes and used data are available at https://github.com/chujiezheng/CoMAE.
Two new infinite classes of APN functions
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we present two new infinite classes of APN functions over $\gf_{2^{2m}}$ and $\gf_{2^{3m}}$, respectively. The first one is with bivariate form and obtained by adding special terms, $\sum(a_ix^{2^i}y^{2^i},b_ix^{2^i}y^{2^i})$, to a known class of APN functions by {G{ö}lo{ǧ}lu} over $\gf_{2^m}^2$. The second one is of the form $L(z)^{2^m+1}+vz^{2^m+1}$ over $\gf_{2^{3m}}$, which is a generalization of one family of APN functions by Bracken et al. [Cryptogr. Commun. 3 (1): 43-53, 2011]. The calculation of the CCZ-invariants $Γ$-ranks of our APN classes over $\gf_{2^8}$ or $\gf_{2^9}$ indicates that they are CCZ-inequivalent to all known infinite families of APN functions. Moreover, by using the code isomorphism, we see that our first APN family covers an APN function over $\gf_{2^8}$ obtained through the switching method by Edel and Pott in [Adv. Math. Commun. 3 (1): 59-81, 2009].
IRS-Aided Wireless Relaying: Optimal Deployment and Capacity Scaling
Venue: arXiv cs.IT 2021
Year: 2021

In this letter, we consider an intelligent reflecting surface (IRS)-aided wireless relaying system, where a decode-and-forward relay (R) is employed to forward data from a source (S) to a destination (D), aided by M passive reflecting elements. We consider two practical IRS deployment strategies, namely, single-IRS deployment where all reflecting elements are mounted on one single IRS that is deployed near S, R, or D, and multi-IRS deployment where the reflecting elements are allocated over three separate IRSs which are deployed near S, R, and D, respectively. Under the line-of-sight (LoS) channel model, we characterize the capacity scaling orders with respect to an increasing M for the IRS-aided relay system with different IRS deployment strategies. For single-IRS deployment, we show that deploying the IRS near R achieves the highest capacity as compared to that near S or D. While for multi-IRS deployment, we propose a practical cooperative IRS passive beamforming design which is analytically shown to achieve a larger capacity scaling order than the single-IRS deployment (i.e., near R or S/D) when M is sufficiently large. Numerical examples are provided, which validate our theoretical results.
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in training data or domain shift. Recent work has tied these shortcomings to beam search -- the de facto standard inference algorithm in NMT -- and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this paper, we empirically investigate the properties of MBR decoding on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and token frequency bias, owing to the MT metrics used as utility functions, but that MBR also increases robustness against copy noise in the training data and domain shift.
A deep learning approach for inverse design of the metasurface for dual-polarized waves
Venue: arXiv cs.LG 2021
Year: 2021

Compared to the conventional metasurface design, machine learning-based methods have recently created an inspiring platform for an inverse realization of the metasurfaces. Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves. To automatically generate metasurfaces in a wide range of working frequencies from 4 to 45 GHz, we deliberately design an 8 ring-shaped pattern in such a way that the unit-cells generated in the dataset can produce single or multiple notches in the desired working frequency band. Compared to the general approach, whereby the final metasurface structure may be formed by any randomly distributed "0" and "1", we propose here a restricted output structure. By restricting the output, the number of calculations will be reduced and the learning speed will be increased. Moreover, we have shown that the accuracy of the network reaches 91\%. Obtaining the final unit cell directly without any time-consuming optimization algorithms for both TE and TM polarized waves, and high average accuracy, promises an effective strategy for the metasurface design; thus, the designer is required only to focus on the design goal.
Federated Learning With Highly Imbalanced Audio Data
Venue: arXiv cs.SD 2021
Year: 2021

Federated learning (FL) is a privacy-preserving machine learning method that has been proposed to allow training of models using data from many different clients, without these clients having to transfer all their data to a central server. There has as yet been relatively little consideration of FL or other privacy-preserving methods in audio. In this paper, we investigate using FL for a sound event detection task using audio from the FSD50K dataset. Audio is split into clients based on uploader metadata. This results in highly imbalanced subsets of data between clients, noted as a key issue in FL scenarios. A series of models is trained using `high-volume' clients that contribute 100 audio clips or more, testing the effects of varying FL parameters, followed by an additional model trained using all clients with no minimum audio contribution. It is shown that FL models trained using the high-volume clients can perform similarly to a centrally-trained model, though there is much more noise in results than would typically be expected for a centrally-trained model. The FL model trained using all clients has a considerably reduced performance compared to the centrally-trained model.
A pragmatic approach for designing transparent WDM optical networks with multi-objectives
Venue: arXiv cs.NI 2021
Year: 2021

In facing with the explosive Internet traffic growth, optical transport networks based on WDM technologies forming the core part of Internet infrastructure carrying multi-Tb/s has to be re-considered from both designing, planning, operation and management perspectives to attain greater efficiency. Thanks to the convergence of significant advances in optical transmission technologies, and photonic switching, transparent (all-optical) architecture has come into practice, paving the way for eliminating the over-utilization of costly optical-electrical-optical (O-E-O) interfaces and hence, yielding remarkable savings of cost and energy consumption compared to opaque architecture. Traditional designs for transparent optical networks based on single-objective optimization model aiming at optimizing solely a single performance metric appears to be insufficient to capture the nuances of practical designs while conventional multi-objective approach tends to reach (non-) optimal solutions. Different from existing works, we present a new framework for multi-objective WDM network designs capturing several goals on one hand and on the other hand, achieving optimal solutions. Moreover, our proposal exploits the characteristics of each constituent objectives to lay the foundation for setting up weight coefficient so that the order of optimization is guaranteed. Equally important, our proposal is pragmatic in the sense that the complexity of the optimization model remains the same as the single-objective model while the quality of solution has been greatly improved. We have extensively tested realistic optical core networks topologies, that is, COST239 and NSFNET, with various network traffic conditions and it turns out that our design brings about a saving of wavelength link usage up to roughly $28\%$ in the most favorable cases while $14\%$ is expected for the least favorable cases.
WOVe: Incorporating Word Order in GloVe Word Embeddings
Venue: arXiv cs.CL 2021
Year: 2021

Word vector representations open up new opportunities to extract useful information from unstructured text. Defining a word as a vector made it easy for the machine learning algorithms to understand a text and extract information from. Word vector representations have been used in many applications such word synonyms, word analogy, syntactic parsing, and many others. GloVe, based on word contexts and matrix vectorization, is an ef-fective vector-learning algorithm. It improves on previous vector-learning algorithms. However, the GloVe model fails to explicitly consider the order in which words appear within their contexts. In this paper, multiple methods of incorporating word order in GloVe word embeddings are proposed. Experimental results show that our Word Order Vector (WOVe) word embeddings approach outperforms unmodified GloVe on the natural lan-guage tasks of analogy completion and word similarity. WOVe with direct concatenation slightly outperformed GloVe on the word similarity task, increasing average rank by 2%. However, it greatly improved on the GloVe baseline on a word analogy task, achieving an average 36.34% improvement in accuracy.
Can we assess research using open scientific knowledge graphs? A case study within the Italian National Scientific Qualification
Venue: arXiv cs.DL 2021
Year: 2021

The need for open scientific knowledge graphs is ever increasing. While there are large repositories of open access articles and free publication indexes, there are still few free knowledge graphs exposing citation networks, and often their coverage is partial. Consequently, most evaluation processes based on citation counts rely on commercial citation databases. Things are changing thanks to the Initiative for Open Citations (I4OC, https://i4oc.org) and the Initiative for Open Abstracts (I4OA, https://i4oa.org), whose goal is to campaign for scholarly publishers to open the reference lists and the other metadata of their articles. This paper investigates the growth of the open bibliographic metadata and open citations in two scientific knowledge graphs, OpenCitations' COCI and Crossref, with an experiment on the Italian National Scientific Qualification (NSQ), the National process for University Professor qualification which uses data from commercial indexes. We simulated the procedure by only using such open data and explored similarities and differences with the official results. The outcomes of the experiment show that the amount of open bibliographic metadata and open citation data currently available in the two scientific knowledge graphs adopted is not yet enough for obtaining results similar to those provided using commercial databases.
Toward Designing Social Human-Robot Interactions for Deep Space Exploration
Venue: arXiv cs.RO 2021
Year: 2021

In planning for future human space exploration, it is important to consider how to design for uplifting interpersonal communications and social dynamics among crew members. What if embodied social robots could help to improve the overall team interaction experience in space? On Earth, social robots have been shown effective in providing companionship, relieving stress and anxiety, fostering connection among people, enhancing team performance, and mediating conflicts in human groups. In this paper, we introduce a set of novel research questions exploring social human-robot interactions in long-duration space exploration missions.
A multimodal deep learning framework for scalable content based visual media retrieval
Venue: arXiv cs.LG 2021
Year: 2021

We propose a novel, efficient, modular and scalable framework for content based visual media retrieval systems by leveraging the power of Deep Learning which is flexible to work both for images and videos conjointly and we also introduce an efficient comparison and filtering metric for retrieval. We put forward our findings from critical performance tests comparing our method to the predominant conventional approach to demonstrate the feasibility and efficiency of the proposed solution with best practices, possible improvements that may further augment the ability of retrieval architectures.
Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing
Venue: arXiv cs.CL 2021
Year: 2021

We describe models focused at the understudied problem of translating between monolingual and code-mixed language pairs. More specifically, we offer a wide range of models that convert monolingual English text into Hinglish (code-mixed Hindi and English). Given the recent success of pretrained language models, we also test the utility of two recent Transformer-based encoder-decoder models (i.e., mT5 and mBART) on the task finding both to work well. Given the paucity of training data for code-mixing, we also propose a dependency-free method for generating code-mixed texts from bilingual distributed representations that we exploit for improving language model performance. In particular, armed with this additional data, we adopt a curriculum learning approach where we first finetune the language models on synthetic data then on gold code-mixed data. We find that, although simple, our synthetic code-mixing method is competitive with (and in some cases is even superior to) several standard methods (backtranslation, method based on equivalence constraint theory) under a diverse set of conditions. Our work shows that the mT5 model, finetuned following the curriculum learning procedure, achieves best translation performance (12.67 BLEU). Our models place first in the overall ranking of the English-Hinglish official shared task.